1. Field
Methods and apparatuses consistent with exemplary embodiments relate to detecting moving objects, and more particularly, to detecting moving objects by determining a background pixel and a foreground pixel from a video captured by an imaging device.
2. Description of the Related Art
In the related art CCTV camera security technology, a manually operated physical security system is used. In such a system, after a video captured by a camera is transferred via a transmission network, a user manually monitors security circumstances through a monitor in real time or search through a recording medium storing recorded videos (e.g., a DVD) later.
However, due to the rapid increase in the number of CCTV cameras used in such a video security system, an enormous amount of human labor may be required to monitor required areas in real time or to analyze captured videos. Also, there is an increasing demand for a real-time monitoring system instead of a post-process monitoring system. Along with the rapid development of information technology, intelligent video surveillance systems that combine various technologies have been studied recently.
Intelligent video security technologies may be applied in various fields. For example, they may be used in national facilities, e.g., airports, military, harbors, roads, and bridges, as well as in subways, buildings, stadiums, parking lots, casinos, automobiles, mobile devices, etc.
A key feature of an intelligent video security system includes detecting a moving object in an input video, and lots of research has been conducted to improve the feature. In most research, a background modeling technology generates a model of a background in a unit of a pixel. This modeling method may be divided into parametric background modeling and non-parametric background modeling. One simple approach to implement the parametric background modeling includes calculating a time axis average of a background and detecting the input video based on difference between the input video and a background video. Another approach is Gaussian modeling which is expressed using an average and a variance.
However, a moving background (e.g., swaying trees or waves) is unlikely to be appropriately removed in the parametric background modeling, a method of using a plurality of models has been proposed. For example, a background probability may be statistically modeled by modeling a change in pixel values, which are sequentially input according to the Gaussian mixture model, with K Gaussian distributions.